Lab 7

Author

Hewan Weldai

Demo

#loading packages
library(tidyverse)
library(lubridate)
library(dplyr)
library(ggplot2)
#import data
 download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv", 
               destfile = "data/time_series_covid19_confirmed_global.csv")
#load data 
time_series_confirmed <- read_csv("data/time_series_covid19_confirmed_global.csv")|>
  rename(Province_State = "Province/State", Country_Region = "Country/Region")

Data Tidying - Pivoting

#convert to long format
time_series_confirmed_long <- time_series_confirmed |> 
  pivot_longer(-c(Province_State, Country_Region, Lat, Long),
   names_to = "Date", values_to = "Confirmed") 

Dates and Time

#Change the format of Date
time_series_confirmed_long$Date <- mdy(time_series_confirmed_long$Date)

Making Graphs From the Time Series Data

#Summarize the country date to count up thee individual state data for the US 
time_series_confirmed_long|> 
  group_by(Country_Region, Date) |> 
  summarise(Confirmed = sum(Confirmed)) |>
  filter (Country_Region == "US") |>
  ggplot(aes(x = Date,  y = Confirmed)) +
   geom_point() +
   geom_line() +
   ggtitle("US COVID-19 Confirmed Cases")

#Many countries on the same graph
time_series_confirmed_long |> 
    group_by(Country_Region, Date) |> 
    summarise(Confirmed = sum(Confirmed)) |> 
    filter (Country_Region %in% c("China","France","Italy", 
                                "Korea, South", "US")) |> 
    ggplot(aes(x = Date,  y = Confirmed, color = Country_Region)) + 
      geom_point() +
      geom_line() +
      ggtitle("COVID-19 Confirmed Cases")

#make new table with the daily counts 
time_series_confirmed_long_daily <-time_series_confirmed_long |> 
    group_by(Country_Region, Date) |> 
    summarise(Confirmed = sum(Confirmed)) |> 
    mutate(Daily = Confirmed - lag(Confirmed, default = first(Confirmed )))
#Graph with US data 
time_series_confirmed_long_daily |> 
    filter (Country_Region == "US") |> 
    ggplot(aes(x = Date,  y = Daily, color = Country_Region)) + 
      geom_point() +
      ggtitle("COVID-19 Confirmed Cases")

#Line graph version
time_series_confirmed_long_daily |> 
    filter (Country_Region == "US") |> 
    ggplot(aes(x = Date,  y = Daily, color = Country_Region)) + 
      geom_line() +
      ggtitle("COVID-19 Confirmed Cases")

#with a curve fit
time_series_confirmed_long_daily |> 
    filter (Country_Region == "US") |> 
    ggplot(aes(x = Date,  y = Daily, color = Country_Region)) + 
      geom_smooth() +
      ggtitle("COVID-19 Confirmed Cases")

#Fit using the Generalized Additive Model (GAM)
time_series_confirmed_long_daily |> 
    filter (Country_Region == "US") |> 
    ggplot(aes(x = Date,  y = Daily, color = Country_Region)) + 
      geom_smooth(method = "gam", se = FALSE) +
      ggtitle("COVID-19 Confirmed Cases")

Animated Graphs with gganimate

#load the packages 
library(gganimate)
library(gifski)
theme_set(theme_bw())

An Animation of the Confirmed Cases in Select Countries

daily_counts <- time_series_confirmed_long_daily |> 
      filter (Country_Region == "US")

p <- ggplot(daily_counts, aes(x = Date,  y = Daily, color = Country_Region)) + 
        geom_point() +
        ggtitle("Confirmed COVID-19 Cases") +
# gganimate lines  
        geom_point(aes(group = seq_along(Date))) +
        transition_reveal(Date) 

# make the animation
 animate(p, renderer = gifski_renderer(), end_pause = 15)

#Save the gif
anim_save("daily_counts_US.gif", p)

Animation of Confirmed Deaths

# This download may take about 5 minutes. You only need to do this once so set `#| eval: false` in your qmd file
download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv", 
  destfile = "data/time_series_covid19_deaths_global.csv")
#data tidying, pivot and time
time_series_deaths_confirmed <- read_csv("data/time_series_covid19_deaths_global.csv")|>
  rename(Province_State = "Province/State", Country_Region = "Country/Region")

time_series_deaths_long <- time_series_deaths_confirmed |> 
    pivot_longer(-c(Province_State, Country_Region, Lat, Long),
        names_to = "Date", values_to = "Confirmed") 

time_series_deaths_long$Date <- mdy(time_series_deaths_long$Date)
#making the animated graph 
p <- time_series_deaths_long |>
  filter (Country_Region %in% c("US","Canada", "Mexico","Brazil","Egypt","Ecuador","India", "Netherlands", "Germany", "China" )) |>
  ggplot(aes(x=Country_Region, y=Confirmed, color= Country_Region)) + 
    geom_point(aes(size=Confirmed)) + 
    transition_time(Date) + 
    labs(title = "Cumulative Deaths: {frame_time}") + 
    ylab("Deaths") +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
# make the animation
animate(p, renderer = gifski_renderer(), end_pause = 15)

Exercises

Exercise 1:

Go through Chapter 5 in R for Data Sciences - Data Tiyding and Pivot](https://r4ds.hadley.nz/data-tidy.html) putting the examples and exerices into your report as in Lab 2 and 3

Chapter 5

5.2

#load packages 
library(tidyverse)
table1
# A tibble: 6 × 4
  country      year  cases population
  <chr>       <dbl>  <dbl>      <dbl>
1 Afghanistan  1999    745   19987071
2 Afghanistan  2000   2666   20595360
3 Brazil       1999  37737  172006362
4 Brazil       2000  80488  174504898
5 China        1999 212258 1272915272
6 China        2000 213766 1280428583
#> # A tibble: 6 × 4
#>   country      year  cases population
#>   <chr>       <dbl>  <dbl>      <dbl>
#> 1 Afghanistan  1999    745   19987071
#> 2 Afghanistan  2000   2666   20595360
#> 3 Brazil       1999  37737  172006362
#> 4 Brazil       2000  80488  174504898
#> 5 China        1999 212258 1272915272
#> 6 China        2000 213766 1280428583

table2
# A tibble: 12 × 4
   country      year type            count
   <chr>       <dbl> <chr>           <dbl>
 1 Afghanistan  1999 cases             745
 2 Afghanistan  1999 population   19987071
 3 Afghanistan  2000 cases            2666
 4 Afghanistan  2000 population   20595360
 5 Brazil       1999 cases           37737
 6 Brazil       1999 population  172006362
 7 Brazil       2000 cases           80488
 8 Brazil       2000 population  174504898
 9 China        1999 cases          212258
10 China        1999 population 1272915272
11 China        2000 cases          213766
12 China        2000 population 1280428583
#> # A tibble: 12 × 4
#>   country      year type           count
#>   <chr>       <dbl> <chr>          <dbl>
#> 1 Afghanistan  1999 cases            745
#> 2 Afghanistan  1999 population  19987071
#> 3 Afghanistan  2000 cases           2666
#> 4 Afghanistan  2000 population  20595360
#> 5 Brazil       1999 cases          37737
#> 6 Brazil       1999 population 172006362
#> # ℹ 6 more rows

table3
# A tibble: 6 × 3
  country      year rate             
  <chr>       <dbl> <chr>            
1 Afghanistan  1999 745/19987071     
2 Afghanistan  2000 2666/20595360    
3 Brazil       1999 37737/172006362  
4 Brazil       2000 80488/174504898  
5 China        1999 212258/1272915272
6 China        2000 213766/1280428583
#> # A tibble: 6 × 3
#>   country      year rate             
#>   <chr>       <dbl> <chr>            
#> 1 Afghanistan  1999 745/19987071     
#> 2 Afghanistan  2000 2666/20595360    
#> 3 Brazil       1999 37737/172006362  
#> 4 Brazil       2000 80488/174504898  
#> 5 China        1999 212258/1272915272
#> 6 China        2000 213766/1280428583
# Compute rate per 10,000
table1 |>
  mutate(rate = cases / population * 10000)
# A tibble: 6 × 5
  country      year  cases population  rate
  <chr>       <dbl>  <dbl>      <dbl> <dbl>
1 Afghanistan  1999    745   19987071 0.373
2 Afghanistan  2000   2666   20595360 1.29 
3 Brazil       1999  37737  172006362 2.19 
4 Brazil       2000  80488  174504898 4.61 
5 China        1999 212258 1272915272 1.67 
6 China        2000 213766 1280428583 1.67 
# Visualize changes over time
ggplot(table1, aes(x = year, y = cases)) +
  geom_line(aes(group = country), color = "grey50") +
  geom_point(aes(color = country, shape = country)) +
  scale_x_continuous(breaks = c(1999, 2000)) # x-axis breaks at 1999 and 2000

5.2.1 Exercises

  1. For each of the sample tables, describe what each observation and each column represents.

For Table 1 the columns represent country, the year of the of cases and population, the number of cases, and the total population for that year.

For table 2 the columns represent country, year of the observation, which type of variable is represented in column 4, the value of the variable mentioned in column 3

For table 3 the columns represent the country which the observations were taken from, the year of the observation, and the ratio of two observation.

  1. Sketch out the process you’d use to calculate the rate for table2 and table3. You will need to perform four operations:

    1. Extract the number of TB cases per country per year.

    2. Extract the matching population per country per year.

    3. Divide cases by population, and multiply by 10000.

    4. Store back in the appropriate place.

    You haven’t yet learned all the functions you’d need to actually perform these operations, but you should still be able to think through the transformations you’d need.

a. Extract the number of TB cases per country per year.

For table2 , I will have to filter out rows where the type = “cases”.

For table3, I have to extract the numerator from the “rate” variable for each row.

b. Extract the matching population per country per year.

For table2, I would filter out the rows where the type = “population”

For table3, I will extract the denominator from the rate variable of each row.

c. Divide cases by population, and multiply by 10000.

For table2 I will divide the value from the part a by the answer from part b.

For table 3, I would multiply the rate variable by 10,000.

d. Store back in the appropriate place.

For table2 I would save the rates in a new rows.

For table 3 I would add another column taking the existing rate column to be per 10,000.

5.3

billboard
# A tibble: 317 × 79
   artist     track date.entered   wk1   wk2   wk3   wk4   wk5   wk6   wk7   wk8
   <chr>      <chr> <date>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 2 Pac      Baby… 2000-02-26      87    82    72    77    87    94    99    NA
 2 2Ge+her    The … 2000-09-02      91    87    92    NA    NA    NA    NA    NA
 3 3 Doors D… Kryp… 2000-04-08      81    70    68    67    66    57    54    53
 4 3 Doors D… Loser 2000-10-21      76    76    72    69    67    65    55    59
 5 504 Boyz   Wobb… 2000-04-15      57    34    25    17    17    31    36    49
 6 98^0       Give… 2000-08-19      51    39    34    26    26    19     2     2
 7 A*Teens    Danc… 2000-07-08      97    97    96    95   100    NA    NA    NA
 8 Aaliyah    I Do… 2000-01-29      84    62    51    41    38    35    35    38
 9 Aaliyah    Try … 2000-03-18      59    53    38    28    21    18    16    14
10 Adams, Yo… Open… 2000-08-26      76    76    74    69    68    67    61    58
# ℹ 307 more rows
# ℹ 68 more variables: wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, wk12 <dbl>,
#   wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, wk17 <dbl>, wk18 <dbl>,
#   wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, wk22 <dbl>, wk23 <dbl>, wk24 <dbl>,
#   wk25 <dbl>, wk26 <dbl>, wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>,
#   wk31 <dbl>, wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>,
#   wk37 <dbl>, wk38 <dbl>, wk39 <dbl>, wk40 <dbl>, wk41 <dbl>, wk42 <dbl>, …
billboard |> 
  pivot_longer(
    cols = starts_with("wk"), 
    names_to = "week", 
    values_to = "rank"
  )
# A tibble: 24,092 × 5
   artist track                   date.entered week   rank
   <chr>  <chr>                   <date>       <chr> <dbl>
 1 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk1      87
 2 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk2      82
 3 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk3      72
 4 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk4      77
 5 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk5      87
 6 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk6      94
 7 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk7      99
 8 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk8      NA
 9 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk9      NA
10 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk10     NA
# ℹ 24,082 more rows
billboard |> 
  pivot_longer(
    cols = starts_with("wk"), 
    names_to = "week", 
    values_to = "rank",
    values_drop_na = TRUE
  )
# A tibble: 5,307 × 5
   artist  track                   date.entered week   rank
   <chr>   <chr>                   <date>       <chr> <dbl>
 1 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk1      87
 2 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk2      82
 3 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk3      72
 4 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk4      77
 5 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk5      87
 6 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk6      94
 7 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk7      99
 8 2Ge+her The Hardest Part Of ... 2000-09-02   wk1      91
 9 2Ge+her The Hardest Part Of ... 2000-09-02   wk2      87
10 2Ge+her The Hardest Part Of ... 2000-09-02   wk3      92
# ℹ 5,297 more rows
billboard_longer <- billboard |> 
  pivot_longer(
    cols = starts_with("wk"), 
    names_to = "week", 
    values_to = "rank",
    values_drop_na = TRUE
  ) |> 
  mutate(
    week = parse_number(week)
  )
billboard_longer
# A tibble: 5,307 × 5
   artist  track                   date.entered  week  rank
   <chr>   <chr>                   <date>       <dbl> <dbl>
 1 2 Pac   Baby Don't Cry (Keep... 2000-02-26       1    87
 2 2 Pac   Baby Don't Cry (Keep... 2000-02-26       2    82
 3 2 Pac   Baby Don't Cry (Keep... 2000-02-26       3    72
 4 2 Pac   Baby Don't Cry (Keep... 2000-02-26       4    77
 5 2 Pac   Baby Don't Cry (Keep... 2000-02-26       5    87
 6 2 Pac   Baby Don't Cry (Keep... 2000-02-26       6    94
 7 2 Pac   Baby Don't Cry (Keep... 2000-02-26       7    99
 8 2Ge+her The Hardest Part Of ... 2000-09-02       1    91
 9 2Ge+her The Hardest Part Of ... 2000-09-02       2    87
10 2Ge+her The Hardest Part Of ... 2000-09-02       3    92
# ℹ 5,297 more rows
billboard_longer |> 
  ggplot(aes(x = week, y = rank, group = track)) + 
  geom_line(alpha = 0.25) + 
  scale_y_reverse()

df <- tribble(
  ~id,  ~bp1, ~bp2,
   "A",  100,  120,
   "B",  140,  115,
   "C",  120,  125
)
df |> 
  pivot_longer(
    cols = bp1:bp2,
    names_to = "measurement",
    values_to = "value"
  )
# A tibble: 6 × 3
  id    measurement value
  <chr> <chr>       <dbl>
1 A     bp1           100
2 A     bp2           120
3 B     bp1           140
4 B     bp2           115
5 C     bp1           120
6 C     bp2           125
who2
# A tibble: 7,240 × 58
   country      year sp_m_014 sp_m_1524 sp_m_2534 sp_m_3544 sp_m_4554 sp_m_5564
   <chr>       <dbl>    <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
 1 Afghanistan  1980       NA        NA        NA        NA        NA        NA
 2 Afghanistan  1981       NA        NA        NA        NA        NA        NA
 3 Afghanistan  1982       NA        NA        NA        NA        NA        NA
 4 Afghanistan  1983       NA        NA        NA        NA        NA        NA
 5 Afghanistan  1984       NA        NA        NA        NA        NA        NA
 6 Afghanistan  1985       NA        NA        NA        NA        NA        NA
 7 Afghanistan  1986       NA        NA        NA        NA        NA        NA
 8 Afghanistan  1987       NA        NA        NA        NA        NA        NA
 9 Afghanistan  1988       NA        NA        NA        NA        NA        NA
10 Afghanistan  1989       NA        NA        NA        NA        NA        NA
# ℹ 7,230 more rows
# ℹ 50 more variables: sp_m_65 <dbl>, sp_f_014 <dbl>, sp_f_1524 <dbl>,
#   sp_f_2534 <dbl>, sp_f_3544 <dbl>, sp_f_4554 <dbl>, sp_f_5564 <dbl>,
#   sp_f_65 <dbl>, sn_m_014 <dbl>, sn_m_1524 <dbl>, sn_m_2534 <dbl>,
#   sn_m_3544 <dbl>, sn_m_4554 <dbl>, sn_m_5564 <dbl>, sn_m_65 <dbl>,
#   sn_f_014 <dbl>, sn_f_1524 <dbl>, sn_f_2534 <dbl>, sn_f_3544 <dbl>,
#   sn_f_4554 <dbl>, sn_f_5564 <dbl>, sn_f_65 <dbl>, ep_m_014 <dbl>, …
who2 |> 
  pivot_longer(
    cols = !(country:year),
    names_to = c("diagnosis", "gender", "age"), 
    names_sep = "_",
    values_to = "count"
  )
# A tibble: 405,440 × 6
   country      year diagnosis gender age   count
   <chr>       <dbl> <chr>     <chr>  <chr> <dbl>
 1 Afghanistan  1980 sp        m      014      NA
 2 Afghanistan  1980 sp        m      1524     NA
 3 Afghanistan  1980 sp        m      2534     NA
 4 Afghanistan  1980 sp        m      3544     NA
 5 Afghanistan  1980 sp        m      4554     NA
 6 Afghanistan  1980 sp        m      5564     NA
 7 Afghanistan  1980 sp        m      65       NA
 8 Afghanistan  1980 sp        f      014      NA
 9 Afghanistan  1980 sp        f      1524     NA
10 Afghanistan  1980 sp        f      2534     NA
# ℹ 405,430 more rows
household
# A tibble: 5 × 5
  family dob_child1 dob_child2 name_child1 name_child2
   <int> <date>     <date>     <chr>       <chr>      
1      1 1998-11-26 2000-01-29 Susan       Jose       
2      2 1996-06-22 NA         Mark        <NA>       
3      3 2002-07-11 2004-04-05 Sam         Seth       
4      4 2004-10-10 2009-08-27 Craig       Khai       
5      5 2000-12-05 2005-02-28 Parker      Gracie     
household |> 
  pivot_longer(
    cols = !family, 
    names_to = c(".value", "child"), 
    names_sep = "_", 
    values_drop_na = TRUE
  )
# A tibble: 9 × 4
  family child  dob        name  
   <int> <chr>  <date>     <chr> 
1      1 child1 1998-11-26 Susan 
2      1 child2 2000-01-29 Jose  
3      2 child1 1996-06-22 Mark  
4      3 child1 2002-07-11 Sam   
5      3 child2 2004-04-05 Seth  
6      4 child1 2004-10-10 Craig 
7      4 child2 2009-08-27 Khai  
8      5 child1 2000-12-05 Parker
9      5 child2 2005-02-28 Gracie

5.4

cms_patient_experience
# A tibble: 500 × 5
   org_pac_id org_nm                           measure_cd measure_title prf_rate
   <chr>      <chr>                            <chr>      <chr>            <dbl>
 1 0446157747 USC CARE MEDICAL GROUP INC       CAHPS_GRP… CAHPS for MI…       63
 2 0446157747 USC CARE MEDICAL GROUP INC       CAHPS_GRP… CAHPS for MI…       87
 3 0446157747 USC CARE MEDICAL GROUP INC       CAHPS_GRP… CAHPS for MI…       86
 4 0446157747 USC CARE MEDICAL GROUP INC       CAHPS_GRP… CAHPS for MI…       57
 5 0446157747 USC CARE MEDICAL GROUP INC       CAHPS_GRP… CAHPS for MI…       85
 6 0446157747 USC CARE MEDICAL GROUP INC       CAHPS_GRP… CAHPS for MI…       24
 7 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI…       59
 8 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI…       85
 9 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI…       83
10 0446162697 ASSOCIATION OF UNIVERSITY PHYSI… CAHPS_GRP… CAHPS for MI…       63
# ℹ 490 more rows
cms_patient_experience |> 
  distinct(measure_cd, measure_title)
# A tibble: 6 × 2
  measure_cd   measure_title                                                    
  <chr>        <chr>                                                            
1 CAHPS_GRP_1  CAHPS for MIPS SSM: Getting Timely Care, Appointments, and Infor…
2 CAHPS_GRP_2  CAHPS for MIPS SSM: How Well Providers Communicate               
3 CAHPS_GRP_3  CAHPS for MIPS SSM: Patient's Rating of Provider                 
4 CAHPS_GRP_5  CAHPS for MIPS SSM: Health Promotion and Education               
5 CAHPS_GRP_8  CAHPS for MIPS SSM: Courteous and Helpful Office Staff           
6 CAHPS_GRP_12 CAHPS for MIPS SSM: Stewardship of Patient Resources             
cms_patient_experience |> 
  pivot_wider(
    names_from = measure_cd,
    values_from = prf_rate
  )
# A tibble: 500 × 9
   org_pac_id org_nm           measure_title CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3
   <chr>      <chr>            <chr>               <dbl>       <dbl>       <dbl>
 1 0446157747 USC CARE MEDICA… CAHPS for MI…          63          NA          NA
 2 0446157747 USC CARE MEDICA… CAHPS for MI…          NA          87          NA
 3 0446157747 USC CARE MEDICA… CAHPS for MI…          NA          NA          86
 4 0446157747 USC CARE MEDICA… CAHPS for MI…          NA          NA          NA
 5 0446157747 USC CARE MEDICA… CAHPS for MI…          NA          NA          NA
 6 0446157747 USC CARE MEDICA… CAHPS for MI…          NA          NA          NA
 7 0446162697 ASSOCIATION OF … CAHPS for MI…          59          NA          NA
 8 0446162697 ASSOCIATION OF … CAHPS for MI…          NA          85          NA
 9 0446162697 ASSOCIATION OF … CAHPS for MI…          NA          NA          83
10 0446162697 ASSOCIATION OF … CAHPS for MI…          NA          NA          NA
# ℹ 490 more rows
# ℹ 3 more variables: CAHPS_GRP_5 <dbl>, CAHPS_GRP_8 <dbl>, CAHPS_GRP_12 <dbl>
cms_patient_experience |> 
  pivot_wider(
    id_cols = starts_with("org"),
    names_from = measure_cd,
    values_from = prf_rate
  )
# A tibble: 95 × 8
   org_pac_id org_nm CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3 CAHPS_GRP_5 CAHPS_GRP_8
   <chr>      <chr>        <dbl>       <dbl>       <dbl>       <dbl>       <dbl>
 1 0446157747 USC C…          63          87          86          57          85
 2 0446162697 ASSOC…          59          85          83          63          88
 3 0547164295 BEAVE…          49          NA          75          44          73
 4 0749333730 CAPE …          67          84          85          65          82
 5 0840104360 ALLIA…          66          87          87          64          87
 6 0840109864 REX H…          73          87          84          67          91
 7 0840513552 SCL H…          58          83          76          58          78
 8 0941545784 GRITM…          46          86          81          54          NA
 9 1052612785 COMMU…          65          84          80          58          87
10 1254237779 OUR L…          61          NA          NA          65          NA
# ℹ 85 more rows
# ℹ 1 more variable: CAHPS_GRP_12 <dbl>
df <- tribble(
  ~id, ~measurement, ~value,
  "A",        "bp1",    100,
  "B",        "bp1",    140,
  "B",        "bp2",    115, 
  "A",        "bp2",    120,
  "A",        "bp3",    105
)
df |> 
  pivot_wider(
    names_from = measurement,
    values_from = value
  )
# A tibble: 2 × 4
  id      bp1   bp2   bp3
  <chr> <dbl> <dbl> <dbl>
1 A       100   120   105
2 B       140   115    NA
df |> 
  distinct(measurement) |> 
  pull()
[1] "bp1" "bp2" "bp3"
df |> 
  select(-measurement, -value) |> 
  distinct()
# A tibble: 2 × 1
  id   
  <chr>
1 A    
2 B    
df |> 
  select(-measurement, -value) |> 
  distinct() |> 
  mutate(x = NA, y = NA, z = NA)
# A tibble: 2 × 4
  id    x     y     z    
  <chr> <lgl> <lgl> <lgl>
1 A     NA    NA    NA   
2 B     NA    NA    NA   
df <- tribble(
  ~id, ~measurement, ~value,
  "A",        "bp1",    100,
  "A",        "bp1",    102,
  "A",        "bp2",    120,
  "B",        "bp1",    140, 
  "B",        "bp2",    115
)
df |>
  pivot_wider(
    names_from = measurement,
    values_from = value
  )
# A tibble: 2 × 3
  id    bp1       bp2      
  <chr> <list>    <list>   
1 A     <dbl [2]> <dbl [1]>
2 B     <dbl [1]> <dbl [1]>
df |> 
  group_by(id, measurement) |> 
  summarize(n = n(), .groups = "drop") |> 
  filter(n > 1)
# A tibble: 1 × 3
  id    measurement     n
  <chr> <chr>       <int>
1 A     bp1             2

Exercise 2

Instead of making a graph of 5 countries on the same graph as in the above example, use facet_wrap with scales="free_y".

time_series_confirmed_long |> 
    group_by(Country_Region, Date) |> 
    summarise(Confirmed = sum(Confirmed)) |> 
    filter (Country_Region %in% c("China","France","Italy", 
      "Korea, South", "US")) |>
  
    ggplot(aes(x = Date,  y = Confirmed, color = Country_Region)) + 
      geom_point() +
      geom_line() +
  facet_wrap(~ Country_Region, scales = "free_y") +
      ggtitle("COVID-19 Confirmed Cases")

Exercise 3

Using the daily count of confirmed cases, make a single graph with 5 countries of your choosing.

time_series_confirmed_long |> 
    group_by(Country_Region, Date) |> 
    summarise(Confirmed = sum(Confirmed)) |> 
    filter (Country_Region %in% c("Albania","Eritrea","Burma", 
      "Afghanistan", "Japan")) |>
  
    ggplot(aes(x = Date,  y = Confirmed, color = Country_Region)) + 
      geom_point() +
      geom_line() +
      ggtitle("COVID-19 Confirmed Cases")

Exercise 4

Plot the cumulative deaths in the US, Canada and Mexico (you will need to download time_series_covid19_deaths_global.csv)

time_series_deaths_long |> 
    group_by(Country_Region, Date) |> 
    summarise(Confirmed = sum(Confirmed)) |> 
    filter (Country_Region %in% c("Canada","Mexico", "US")) |> 
    ggplot(aes(x = Date,  y = Confirmed, color = Country_Region)) + 
      geom_point() +
      geom_line() +
      ggtitle("COVID-19 Confirmed Deaths")

Exercise 5

Make a graph with the countries of your choice using the daily deaths data

time_series_deaths_long |> 
    group_by(Country_Region, Date) |> 
    summarise(Confirmed = sum(Confirmed)) |> 
    filter (Country_Region %in% c("Albania","Eritrea","Burma", 
      "Afghanistan", "Japan")) |>
  
    ggplot(aes(x = Date,  y = Confirmed, color = Country_Region)) + 
      geom_point() +
      geom_line() +
      ggtitle("COVID-19 Confirmed Cases")

Exercise 6

Make an animation of your choosing (do not use a graph with geom_smooth)

p <- time_series_deaths_long |>
  filter (Country_Region %in% c("US","Canada", "Mexico","Burma","Eritrea","Albania","Japan", "Afghanistan", "France", "Ghana" )) |>
  ggplot(aes(x=Country_Region, y=Confirmed, color= Country_Region)) + 
    geom_point(aes(size=Confirmed)) + 
    transition_time(Date) + 
    labs(title = "Cumulative Deaths: {frame_time}") + 
    ylab("Deaths") +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
# make the animation
animate(p, renderer = gifski_renderer(), end_pause = 15)